Crowdfunding and sustainable development: A systematic review
Bibliographic record
Abstract
• This paper offers a bibliometric review of 148 articles on crowdfunding and SDGs. • Using performance analysis and science mapping, we identify four research clusters. • This study contributes to the literature on sustainability-oriented crowdfunding. • It offers practical implications and a detailed roadmap for future research. This study employs bibliometric analysis to explore the evolving role of crowdfunding in financing sustainable development goals (SDGs). Analyzing 148 peer-reviewed articles (2014–2024), it identifies key trends, influential contributors, and thematic clusters in academic discourse. Findings reveal a surge in research post-2020, with a focus on entrepreneurial finance, environmental sustainability, and financial innovation. Equity crowdfunding and FinTech emerge as pivotal in bridging sustainability-related funding gaps. Cluster analysis highlights four major research areas: financial innovation's role in sustainability, crowdfunding's contribution to SDGs (especially post-COVID-19), microfinancing and financial inclusion for SMEs, and ESG integration in entrepreneurial finance. Despite these advances, significant research gaps remain, particularly the need for longitudinal studies to assess the long-term impacts of crowdfunding on sustainability, as well as a deeper understanding of the ethical implications surrounding governance and backer protection on crowdfunding platforms. This study contributes to the growing body of literature on sustainability-oriented crowdfunding by offering a detailed roadmap for future research and practical implications for scholars and practitioners alike.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.044 | 0.044 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".